spateo.digitization.contour#
Written by @Jinerhal, adapted by @Xiaojieqiu.
Module Contents#
Functions#
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Generate matrix/image of spatial clusters with distinct labels/colors. |
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Extract contour(s) for area(s) formed by buckets of the same spatial cluster. |
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Set the domains for each bucket based on spatial clusters. Use adata object of low resolution for contour |
- spateo.digitization.contour.gen_cluster_image(adata: anndata.AnnData, bin_size: int | None = None, spatial_key: str = 'spatial', cluster_key: str = 'scc', label_mapping_key: str = 'cluster_img_label', cmap: str = 'tab20', show: bool = True) numpy.ndarray[source]#
Generate matrix/image of spatial clusters with distinct labels/colors.
- Parameters:
- adata
The adata object used to create the matrix/image for clusters.
- bin_size
The size of the binning.
- spatial_key
The key name of the spatial coordinates in adata.obs
- cluster_key
The key name of the spatial cluster in adata.obs
- label_mapping_key
The key name to store the label index values, mapped from the cluster names in adata.obs. Note that background is 0 so label_mapping_key starts from 1.
- cmap
The colormap that will be used to draw colors for the resultant cluster image.
- show
Whether to visualize the cluster image.
- Returns:
- A numpy array that stores the image of clusters, each with a distinct color. When show
is True, plt.imshow(cluster_rgb_image) will be used to plot the clusters each with distinct labels prepared from the designated cmap.
- Return type:
cluster_label_image
- spateo.digitization.contour.extract_cluster_contours(cluster_label_image: numpy.ndarray, cluster_labels: int | List, bin_size: int, k_size: float = 2, min_area: float = 9, close_kernel: int = cv2.MORPH_ELLIPSE, show: bool = True) Tuple[Tuple, numpy.ndarray, numpy.ndarray][source]#
Extract contour(s) for area(s) formed by buckets of the same spatial cluster.
- Parameters:
- cluster_label_image
the image that sets the pixels of the cluster of interests as the front color (background is 0).
- cluster_labels
The label value(s) of clusters of interests.
- bin_size
The size of the binning.
- k_size
Kernel size of the elliptic structuring element.
- min_area
Minimal area threshold corresponding to the resulting contour(s).
- close_kernel
The value to indicate the structuring element. By default, we use a circular structuring element.
- show
Visualize the result.
- Returns:
The Tuple coordinates of contours identified. cluster_image_close: The resultant image of the area of interest with small area removed. cluster_image_contour: The resultant image of the contour, generated from cluster_image_close.
- Return type:
contours
- spateo.digitization.contour.set_domains(adata_high_res: anndata.AnnData, adata_low_res: anndata.AnnData | None = None, spatial_key: str = 'spatial', cluster_key: str = 'scc', domain_key_prefix: str = 'domain', bin_size_high: int | None = None, bin_size_low: int | None = None, k_size: float = 2, min_area: float = 9) None[source]#
- Set the domains for each bucket based on spatial clusters. Use adata object of low resolution for contour
identification but adata object of high resolution for domain assignment.
- Parameters:
- adata_high_res
The anndata object in high spatial resolution. The adata with smaller binning (or single cell segmetnation) is more suitable to define more fine grained spatial domains.
- adata_low_res
The anndata object in low spatial resolution. When using data with big binning, it can often produce better spatial domain clustering results with the scc method and thus domain/domain contour identification.
- spatial_key
The key in .obsm of the spatial coordinate for each bucket. Should be same key in both adata_high_res and adata_low_res.
- cluster_key
The key in .obs (adata_low_res) to the spatial cluster.
- domain_key_prefix
The key prefix in .obs (in adata_high_res) that will be used to store the spatial domain for each bucket. The full key name will be set as: domain_key_prefix + “_” + cluster_key.
- bin_size_low
The binning size of the adata_high_res object.
- bin_size_low
The binning size of the adata_low_res object (only works when adata_low_res is provided).
- k_size
Kernel size of the elliptic structuring element.
- min_area
Minimal area threshold corresponding to the resulting contour(s).
- Returns:
Nothing but update the adata_high_res with the domain in domain_key_prefix + “_” + cluster_key.